Trustworthy and Explainable LLM Security Frameworks

Abstract

Large Language Models (LLMs) are increasingly deployed across critical domains, from cybersecurity and healthcare to finance and education. While their capabilities have transformed automation and decision-making, these systems face significant challenges related to trust, security, and explainability. As adversarial attacks, data poisoning, and prompt manipulation continue to evolve, the lack of transparency in LLM decision-making undermines user confidence and regulatory compliance. This chapter introduces the concept of trustworthy and explainable LLM security frameworks, which integrate principles of interpretability, accountability, and robust defense mechanisms. By combining security-by-design approaches with explainable artificial intelligence (XAI) techniques, these frameworks aim to mitigate vulnerabilities while ensuring transparency in system outputs. The discussion highlights architectural considerations, governance models, and best practices that bridge the gap between technical resilience and human-centric trust. Furthermore, the chapter explores how explainability enhances threat detection, incident response, and ethical assurance, making LLMs more reliable in high-stakes environments. Ultimately, establishing trustworthy and explainable LLM security frameworks is not only a technical necessity but also a societal imperative for the responsible adoption of next-generation AI systems.

Ähnliche Arbeiten